Efficiency analysis of importance sampling in deep submicron STT-RAM design using uncontrollable industry-compatible model parameter

In this paper, we first analyze the efficiency of importance sampling (IS) method in spin transfer torque random access memory (STT-RAM) design with industry-compatible model parameter. Commonly used normal fitting method cannot estimate the yield accurately unless an output distribution follows the Gaussian distribution. The efficiency of IS method is significantly degraded when industry-compatible model parameters are used because most variables affected by process variation are not controllable. With industry-compatible 45-nm model parameters, Monte Carlo HSPICE simulation results show that the required number of simulations to satisfy error rate less than 5% should be greater than 50,000.